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Abstract

Background

There is little research on the relationship between key socioeconomic variables and
alcohol related harms in Australia. The aim of this research was to examine the relationship
between income inequality and the rates of alcohol-attributable hospitalisation and
death at a local-area level in Australia.

Method

We conducted a cross sectional ecological analysis at a Local Government Area (LGA)
level of associations between data on alcohol caused harms and income inequality data
after adjusting for socioeconomic disadvantage and remoteness of LGAs.

The main outcome measures used were matched rate ratios for four measures of alcohol
caused harm; acute (primarily related to the short term consequences of drinking)
and chronic (primarily related to the long term consequences of drinking) alcohol-attributable
hospitalisation and acute and chronic alcohol-attributable death. Matching was undertaken
using control conditions (non-alcohol-attributable) at an LGA level.

Results

A total of 885 alcohol-attributable deaths and 19467 alcohol-attributable hospitalisations
across all LGAs were available for analysis. After weighting by the total number of
cases in each LGA, the matched rate ratios of acute and chronic alcohol-attributable
hospitalisation and chronic alcohol-attributable death were associated with the squared
centred Gini coefficients of LGAs. This relationship was evident after adjusting for
socioeconomic disadvantage and remoteness of LGAs. For both measures of hospitalisation
the relationship was curvilinear; increases in income inequality were initially associated
with declining rates of hospitalisation followed by large increases as the Gini coefficient
increased beyond 0.15. The pattern for chronic alcohol-attributable death was similar,
but without the initial decrease. There was no association between income inequality
and acute alcohol-attributable death, probably due to the relatively small number
of these types of death.

Conclusion

We found a curvilinear relationship between income inequality and the rates of some
types of alcohol-attributable hospitalisation and death at a local area level in Australia.
While alcohol-attributable harms generally increased with increasing income inequality,
alcohol-attributable hospitalisations actually showed the reverse relationship at
low levels of income inequality. The curvilinear patterns we observed are inconsistent
with monotonic trends found in previous research making our findings incompatible
with previous explanations of the relationship between income inequality and health
related harms.

Background

The harms caused by drinking are mediated by a variety of social and contextual factors
operating at both individual and community levels. For example, social class (as represented
by occupational categories) has been shown to independently account for the occurrence
of alcohol caused harms [1]. Other socio-economic status variables such as income and spending power have also
been shown to be associated with different types of alcohol caused death with lowered
socioeconomic status often associated with higher likelihood of alcohol caused death
[2]. Previous Australian work has demonstrated the extent to which these effects are
evident in studies using data available at an ecologic level [3,4]. This work has shown that the relationship between drinking and alcohol caused hospitalisation
in local areas is mediated by factors such as income levels and unemployment [3]. These findings in the alcohol literature are consistent with those evident in the
wider field of social epidemiology where social contextual factors such as employment
status, level of educational attainment an income have been shown to be related to
a variety of health outcomes, such as mortality [5,6]. Again, lowered socio-economic status has generally been associated with poorer health
outcomes, but this pattern does vary across specific conditions [7].

A considerable amount of work in social epidemiology has focused upon income inequality
or disparity. Income disparities arise when income is unequally distributed across
a given population, irrespective of the absolute income levels of the population.
Income inequality has been shown to be associated with a variety of health and social
outcomes including rates of all-cause mortality [8], violent crime [9], and life expectancy [see [10], for a review]. In general the pattern found is such that increases in income inequality
are associated with increases in morbidity or mortality. However, the relationship
between income inequality and health remains controversial, with some recent reviews
and studies suggesting that the evidence of an association between income inequality
and mortality is equivocal at best [10,11]. Nevertheless, some research by particular groups has continued to show an association
between income inequality and measures of health [e.g. [12]].

Income inequality can be measured in a number of ways, but is most often measured
through the Gini coefficient in the field of health research [13]. The Gini coefficient ranges from 0 (equitable income distribution) to 1 (maximum
income inequality), representing the proportion of the population in specific income
categories relative to the total population's income, and can be applied across time
and place.

Various mechanisms have been postulated through which income inequality may manifest
an effect on health outcomes [10,14]. Typically the postulated mechanisms are indirect in that income inequality is thought
to be associated with social-contextual processes that may result in biased policy
producing 'social capital' favouring the wealthy in an area. Such social capital may
be expressed in policy terms such as better resource allocation, but may also reflect
better social connectedness (e.g. having others who can be trusted) for those at higher
income levels. Direct effects have also been postulated, whereby living near rich
neighbours produces a kind of 'economic envy' among poorer people that results in
greater stress and therefore poorer health (and possibly more drinking) [10]. These effects may be manifested in risk behaviours for poorer health such as alcohol
or other drug consumption. These questions are of fundamental interest to public policy
with recent debate in Australia, for example, focusing on questions of mechanisms
for social improvement. In this debate understanding the interrelationships between
social-contextual variables and health outcomes is seen as crucial [15]. Indeed, the recently-elected Australian Government has convened a National Preventive
Health Taskforce that has a specific mandate in the area of alcohol harm reduction
that sits within the Government's agenda on social inclusion [16].

Galea and colleagues have explored the relationship between income inequality and
alcohol and other drug use and related harms (primarily) in New York City. Their work
has shown positive associations between neighbourhood income inequality and drug overdose
mortality [17], and alcohol and cannabis use [18], that were independent of other neighbourhood characteristics and individual-level
variables such as personal income. However, as is the case with the general mortality
studies reviewed by Lynch [11], there have been some inconsistent results in terms of the association between alcohol
and drug related harms and income inequality. For example, Blomgren et al [19] found no significant association between area-level income inequality and alcohol
caused mortality in a Finnish study. This apparent inconsistency between studies may
be due to the small variability in income inequality evident in Finland (Gini coefficients
ranging between 0.20 and 0.24). Indeed, the small variation in intra-country income
inequalities in studied countries other than the USA has been proposed as an explanation
for the mixed pattern of results found for income inequality in social epidemiology
more broadly [10].

To our knowledge there has been no study of the relationship between income inequality
and alcohol and drug related outcomes in Australia, despite the availability of a
variety of alcohol and drug related data amenable to such an analysis. This research
aimed to begin to address this gap by examining the relationship between income inequality
and the rates of alcohol-attributable death and hospitalisation in Australia. On the
basis of Galea et al's findings we expected this relationship to be linear with increasing income inequality
associated with increasing alcohol-attributable harm. As indicated, this relationship
is important in the context of recent developments in Australia where understanding
the relationship between social-contextual variables and health outcomes is firmly
on the policy agenda.

Methods

We used an ecologic design in which area-level variables were extracted from a series
of datasets available from the Australian Bureau of Statistics (ABS) and the Australian
Institute of Health and Welfare (AIHW). The research was approved by the Monash University
Standing Committee on Ethics in Research involving Humans.

Geographic units

We analysed data at the Local Government Area (LGA) level as LGA boundaries correspond
to the administrative areas for which local governments are responsible. Local government
is not only widely understood in defining community areas in Australia but also plays
an important role in Australian alcohol policy; for example, in determining drinking
by-laws, planning issues with respect to licensed premises, and safer city initiatives.
This is also the level at which local community initiatives often operate [e.g. local
liquor licensee accords [20]]. For these reasons the LGA was selected as the preferred geographic unit.

Main outcome measures

Two types of alcohol related harm were examined; alcohol-attributable hospitalisation
and alcohol-attributable mortality. Data on hospitalisations was obtained from the
AIHW's National Hospital Morbidity Database (NHMD) which is a compilation of clinical
information on hospital separations across all public and almost all private hospitals
in Australia. Mortality data were sourced from the ABS Mortality Datafile, which is
a compilation of details of all Australian deaths obtained from state and territory
Death Registries. Both data sources contain information on age, sex, principal diagnosis,
external cause and LGA of residence for all cases. Unfortunately the NHMD does not
include detailed data on place of residence for Queensland or South Australian hospitalisations,
meaning that these two states were not included in the analysis of alcohol-attributable
hospitalisations. Principle diagnosis and any applicable external causes are coded
on both datasets according to International Classification of Diseases 10threvision, Australian Modification (ICD-10-AM). Hospitalisation data analysed in this report
cover the 1999/2000 fiscal year while the mortality data were obtained for the 2000
calendar year.

Aggregate measures of hospitalisation and death attributable to risky/high-risk drinking
at an LGA level were developed by first extracting cases with a principle or external
cause diagnosis indicating the cause of ill-health or death was wholly attributable
to alcohol. These conditions were taken from those identified in a meta-analysis originally
published by English et al [21] These cases were then categorised as acute (primarily related to the effects of risky
drinking in the short-term, e.g. alcoholic beverage poisoning) or chronic (primarily
related to the effects of risky drinking in the long-term, eg alcoholic liver cirrhosis)
on the basis of the likely drinking pattern that resulted in hospitalisation or death,
as recommended by the World Health Organisation [22]. We also extracted a series of cases identified as largely unrelated to drinking.
For hospitalisations, these were acute appendicitis, diverticulitis, hyperplasia of
prostate, genital prolapse and osteoarthritis – each identified as non-alcohol-related
by previous Australian research [3]. Causes of death which are known to be largely unaffected by alcohol consumption
are relatively rare in Australia, for mortality data therefore, controls were defined
as cases for which the alcohol aetiologic fraction for risky/high-risk drinking was
zero but which may have attracted an alcohol aetiologic fraction for low-risk drinking
(e.g. ischaemic heart disease).

Once extracted, these measures were aggregated by Australian LGAs. They were then
age and sex standardised via indirect standardisation using estimated resident populations
from the 2001 census. Initial inspection of the outcome data showed that the resultant
standardised morbidity ratios (SMRs) were highly skewed at an LGA level for both alcohol-attributable
and control conditions. As a consequence these data were log-transformed and then
a matched rate ratio was generated for each measure (acute and chronic) of hospitalisation
and death. This matched rate ratio was the log-transformed rate of the alcohol SMR
divided by the control SMR for each LGA.

Predictor variables

Three predictor variables were developed for LGAs and included as predictors. First,
a Gini coefficient was developed using detailed reported weekly income information
obtained from the 2001 ABS census. Second, the socioeconomic characteristics of areas
were indexed through the ABS Socio-Economic Index for Areas, SEIFA disadvantage score [23]. This score is census-derived and summarises the socioeconomic disadvantage of areas
focusing on the following area-level characteristics: low income earners; relatively
lower educational attainment; and high unemployment. Low scores show high levels of
disadvantage while high scores show relatively low levels of disadvantage within areas.
Third, the geographic characteristics of areas were indexed through the ABS Accessibility/Remoteness Index for Australia, ARIA score [24]. This index summarised area-level characteristics for census collector districts
(CDs) in terms of the distance of the CD from access to the widest range of goods
and services and opportunities for social interaction. Mean ARIA scores can be calculated
for LGAs (compilations of CDs) and then classified according to the following five
categories developed by the ABS: Major cities; Inner Regional Australia; Outer Regional
Australia; Remote Australia; and Very Remote Australia.

Gini coefficients are calculated as the area under a Lorenz curve plotted with income
categories on the x-axis and the proportion of the total population's income on the
y-axis). In this study we generated Gini coefficients for LGAs in the following way.
First, the number of households within each LGA in each income category was determined
by aggregating across the number of residents and the type of household (e.g. lone
person, group, one family, two families). The midpoint of each income category for
each LGA was then multiplied by the number of households in each income category in
the LGA. However, the income data collected in the Australian census is right-censored
because the largest response category available is $2000. In order to provide a more
parsimonious estimate of the midpoint of this income category we chose a value of
$2250, consistent with the midpoints of other income categories. However, the true
midpoint of this category is still likely to be underestimated. This right-censoring
means that the resultant income distribution will probably show less variation and
produce more conservative estimates of the Gini coefficient than if higher income
values were available. Income categories within LGA were then ranked to form progressive
cumulative totals of numbers of households at each income level. We then numerically
integrated the Lorenz curve of cumulative income vs cumulative households, using a
simple trapezoidal rule algorithm. For each LGA, a Gini coefficient was then calculated
as the difference between 0.5 and the computed area under the LGA's Lorenz curve.

Table 1. Descriptive statistics for the key variables included in analyses

Data analysis

All analyses were conducted using Stata/SE V9. The matched rate ratios for all four
alcohol-attributable outcomes (acute and chronic alcohol-attributable hospitalisations,
acute and chronic alcohol-attributable deaths) described above were entered into a
linear regression as outcome variables with LGA-level Gini coefficient, SEIFA disadvantage
scores (included as decile values) and ARIA category (five levels) entered as predictor
variables. The GINI coefficient was centred (i.e. X = Gini-mean(Gini)) in order to
minimise the correlation between coefficients. There was a large variation evident
in the number of cases occurring in LGAs, related in part to the size of the LGAs.
In order to control for these variations in the models we weighted the models by the
number of cases (both alcohol-attributable and control) using the analytic weights
procedure available in Stata. This weighting involves using the generalised inverse
variance for each LGA in order to account for variation in LGA size. Initial exploration
suggested that the relationship between the centred Gini coefficient and all outcomes
was curvilinear and so we included a quadratic term in the regression analyses. We
also attempted to control for spatial autocorrelation. However, technical difficulties
of adjusting for spatial correlation in a weighted analysis precluded any adjustment
for these effects. Nevertheless, the effects of spatial autocorrelation are typically
small or non-existent in previous studies of alcohol-attributable harm [e.g. [25]].

There was a total of 630 Australian LGAs included in our dataset. The number of events
varied across LGAs with some LGAs having few or zero cases or controls. LGAs with
zero cases or controls for a given outcome measure were not included in the analysis
of that outcome. As hospitalisation data from Queensland and South Australia were
unavailable, LGAs from these two states were not included in the analysis of alcohol-attributable
hospitalisations. The number of LGAs included in each analysis is specified in relation
to each of the models below.

Results

There were 885 alcohol-attributable deaths and 19467 alcohol-attributable hospitalisations
across all LGAs. Table 1 shows the major descriptive characteristics of the LGA-level data included in the
analysis across all of the diagnostic categories, along with the Gini coefficients
included in the different analyses undertaken. The mean value of the Gini coefficient
(the area of the curve deviating from the diagonal) was around 0.18, ranging from
.105 (most equitable) to 0.28 (most inequitable). The remainder of the results section
describes the results of the regression analyses undertaken in relation to each specific
outcome measure, as described above. A sensitivity analysis in which we excluded small
and large LGAs by including only the middle two quartiles of LGAs by population size
showed a more-or-less identical pattern to the results presented below, and is therefore
not reported here 1.

Alcohol-attributable hospitalisation

There was a relatively large number of alcohol-attributable hospitalisations and 373
LGAs with corresponding Gini coefficients available for analysis of the acute alcohol-attributable
hospitalisations and 349 LGAs with corresponding Gini coefficients available for analysis
of the chronic alcohol-attributable hospitalisations. Table 2 shows that there was a highly significant association between the squared centred
Gini coefficient and the rate ratio of both acute and chronic hospitalisations at
an LGA level, after adjusting for SEIFA disadvantage and ARIA scores. The ARIA scores
also showed an interesting curvilinear pattern with decreased rates of acute and chronic
alcohol-attributable hospitalisation for inner and outer regional areas in comparison
to metropolitan areas and increased rates in the remote and very remote areas.

Table 2. Regression coefficients and 95% CIs for the predictor variables included in the model
for alcohol-attributable hospitalisations

Figures 1 and 2 show the raw scores as well as trend lines (plotted using Loess curves of best fit)
of the model-predicted and raw scores for the acute (Figure 1) and chronic (Figure 2) alcohol-attributable hospitalisations. The curvilinear quadratic relationships were
very similar such that with increasing inequality, the rate ratio first decreased
but increased dramatically as the Gini coefficient approached 0.2 for both acute and
chronic alcohol-attributable hospitalisations.

Figure 1.Gini coefficient by matched rate ratio for acute alcohol-attributable hospitalisations
for Australian LGAs in 99/00 fiscal year (trendlines show Loess curves of best fit for model predicted, (solid) and raw scores
(dashed)).

Figure 2.Gini coefficient by matched rate ratio for chronic alcohol-attributable hospitalisations
for Australian LGAs in 99/00 fiscal year (trendlines show Loess curves of best fit for model predicted, (solid) and raw scores
(dashed)).

Alcohol-attributable deaths

Table 1 shows that, compared to alcohol-attributable hospitalisations, there was a much smaller
number of alcohol-attributable deaths available for analysis. However, while there
were relatively few alcohol caused deaths, there was a larger number of LGAs with
corresponding Gini coefficients available for analysis. Table 3 shows that there was a highly significant association between the squared centred
Gini coefficient and the rate ratio of the chronic, but not the acute, alcohol-attributable
deaths. Interestingly, the SEIFA disadvantage scores were associated with acute alcohol-attributable
deaths with the most disadvantaged decile having higher rate ratios than the remaining
deciles, significantly so in comparison to deciles 2–4 (areas of relatively high disadvantage).
In comparison to the Major Cities, the ARIA scores for all other areas were associated
with fewer chronic alcohol-attributable deaths.

Table 3. Regression coefficients and 95% CIs for the predictor variables included in the model
for alcohol-attributable deaths

Figures 3 and 4 show the raw scores as well as trend lines (plotted using Loess curves of best fit)
of the model-predicted and raw scores for the acute (Figure 1) and chronic (Figure 2) alcohol caused deaths. Figure 3 highlights not only the fact that no clear relationship was evident between the Gini
coefficient and acute alcohol-attributable death but also just how sparse the data
were in comparison to the other measures of alcohol-attributable harm. In contrast,
Figure 4 shows the curvilinear quadratic relationship between the Gini coefficient and chronic
alcohol-attributable death. However, unlike the hospitalisation data shown in Figures
1 and 2, there was no evidence of the concave decrease, with a flat relationship evident
until values of the Gini coefficient reach about .17, above which the increase appears
to follow a similar pattern to the hospitalisation data shown in Figures 1 and 2.

Figure 3.Gini coefficient by matched rate ratio for acute alcohol-attributable deaths for Australian
LGAs in 0001 fiscal year (trendlines show Loess curves of best fit for model predicted, (solid) and raw scores
(dashed)).

Figure 4.Gini coefficient by matched rate ratio for chronic alcohol-attributable deaths for
Australian LGAs in 00/01 fiscal year (trendlines show Loess curves of best fit for
model predicted, (solid) and raw scores (dashed)).

Discussion

This study is the first to provide evidence of a relationship between income inequality
and alcohol-attributable harm in Australia. The nature of the relationship was consistent
across acute and chronic alcohol-attributable hospitalisations and was similar for
chronic alcohol-attributable deaths. In general the results showed that increasing
LGA-level income inequality was associated with increasing rates of alcohol-attributable
harm, after adjusting for general socio-economic disadvantage and remoteness of LGAs.
While these relationships appeared strong and robust, there was no evidence of a relationship
between income inequality and acute alcohol-attributable deaths, possibly due to the
relatively small number reported.

The relationship between income inequality and health outcomes such as all-cause mortality
remains controversial [10,14]. However, observed relationships have typically been shown to be a monotonically
increasing function; that is, as income inequality increases so too do rates of ill-health
or death [13]. In this context our findings of a curvilinear function were unexpected; especially
the apparent decline in rates of alcohol-attributable harm with initial increases
in income inequality. In contrast, the significant relationship between income inequality
and chronic alcohol-attributable death appeared to follow a pattern similar to that
found by Galea et al [17] in relation drug overdose. However, Galea et al's matched analysis (where they included other injury death as controls) showed a clear
monotonic trend with no statistically significant differences in odds between different
percentiles of their Gini coefficient. In this way even our findings in relation to
the association between the Gini coefficient and mortality differ from those found
in previous research.

As indicated, the relationship between income inequality and poor health has been
potulated to result from a variety of direct or indirect causal paths [10,14]. Processes such as 'economic envy' may explain the area-level increases in adjusted
rates of alcohol-attributable harms observed in our study at the upper-end of our
Gini coefficient. However, neither direct nor indirect pathways can explain the observed
decline in the rate of alcohol-attributable hospitalisation at the lower values of
the Gini coefficient. It is unlikely that 'economic envy' of neighbours would be worse
for those areas of lesser inequality and it is also unlikely that other forms of social
capital would be lower in these areas, unless there are some unknown confounding factors
for which we did not control. One candidate explanation may be the rapid development
of the urban fringe around Australia's cities which are typically homogenous with
respect to a variety of socio-economic characteristics. However, many of these areas
would be classified as Inner-Regional on the ARIA index and our findings with respect
to income inequality were robust after adjusting for variations in remoteness. An
alternative may be that the homogeneity in areas with low levels of inequality may
produce communities with low levels of diversity and that some smaller increments
in income inequality may produce communities with more diversity and therefore more
interest for community members. The impact of this diversity on drinking behaviours
and health outcomes requires further examination using richer data than that available
for this study. Irrespective, it is difficult to formulate direct policy recommendations
(e.g. interventions designed to reduce income inequalities) on the basis of our findings
as direct intervention to affect income inequality may indeed increase the rate of
alcohol-attributable harms – at least at lower levels of inequality.

The findings in relation to remoteness showed that inner and outer-regional areas
of the country were less likely to experience alcohol-attributable harm than major
cities and remote areas of Australia. These geographic variations contrast with previous
Australian research that has generally shown metropolitan areas to have lower rates
of alcohol-attributable harm than non-metropolitan areas [26]. This contrast may derive from our inclusion of additional levels of remoteness within
the non-metropolitan classification. This suggests that the geographic variation observed
in these previous Australian studies was probably driven largely by the remote and
very remote areas of the country included in the 'non-metropolitan' categories used.
Our finer specification has important implications, suggesting the need for targeting
alcohol programs and policy towards remote and very remote areas of the country.

This study has several limitations. First, the study was ecological meaning that there
is the potential for ecological bias. However, the Gini coefficient, our exposure
variable, is not subject to a classical ecological fallacy because it is a characteristic
the LGA that has been measured. Second, the study was cross-sectional in nature. Ready
interpretation of cross-sectional ecological studies requires the exposure (e.g. drinking,
income inequality) and outcome (e.g. hospitalisation, death) to occur within a similar
timeframe. In this framework it is reasonable to infer that hazardous/high-risk drinking
produced the acute alcohol-attributable outcome for which a relationship to income
inequality was observed (for hospitalisation at least). The same is not necessarily
true of the chronic alcohol-attributable conditions that result from sustained patterns
of risky/high-risk drinking over time. In this case we need to assume relative stability
in persons' residence over time, and relatively static levels of inequality. The validity
of these assumptions is unknown.

Conclusion

In this study we found a curvilinear relationship between income inequality and the
rates of some types of alcohol-attributable hospitalisation and death at a local area
level in Australia. While alcohol-attributable harms generally increased with increasing
income inequality, alcohol-attributable hospitalisations actually showed the reverse
relationship at low levels of income inequality. These curvilinear patterns we observed
are inconsistent with monotonic trends found in previous research making our findings
incompatible with previous explanations of the relationship between income inequality
and health related harms. This has significant implications for public policy initiatives
directed towards reducing income inequalities within LGAs, as it suggests that any
effects on alcohol-attributable harms may not be uniform across different levels of
income inequality.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

All authors contributed to the design of the study. PD led the writing of the manuscript
and DJ and PD led the statistical analyses. TC and PC extracted the data for analysis.
All authors contributed to the drafting of the manuscript. All authors read and approved
the final manuscript.

Acknowledgements

This research was funded by the Alcohol Education and Rehabilitation Foundation. The
first author is in receipt of a Career Development Award from the National Health
and Medical Research Council. There are no conflicts of interest for any of the authors.

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